Data-Driven Prediction and Drift Enhancement with Heterogeneous Graph Analysis:

Predictive model accuracy and dependability maintenance is critical in the quickly changing world of data
driven environments. This work, propose a new framework for drift detection and model updating that
combines machine learning methods such as Long Short-Term Memory (LSTM) networks and Light Gradient
Boosting Machine (LGBM) with statistical tests. We provide a complete strategy that extends to proactive
model adjustment tactics, beginning with the quantitative changes in data distribution that identify drift. Our
experimental approach, which was carried out on simulated datasets intended to replicate temporal variations
in user behavior and market conditions that occur in real life, shows that, when compared to traditional static
models, our method can greatly improve model resilience and reduce prediction error by up to 40%. The study
also looks at the effects of quick model modification, highlighting the need to strike a balance between
predictability and responsiveness. This paper provides a strong methodology for controlling idea drift and
guaranteeing sustained model accuracy in dynamic contexts, adding to the body of knowledge in predictive
analytics. An improved model for forecasting concept drift in sensor data is presented in this work, which is
essential for preserving data quality in dynamic contexts. By combining machine learning with ARIMA, our
model provides accurate drift prediction and detection. Robust performance is ensured by drift detection,
prediction, and preprocessing modules as well as a feedback mechanism. When compared to conventional
models, our approach exhibits better accuracy and early identification. In addition to helping with preventive
maintenance scheduling and cutting costs and downtime, it promises benefits for industries that depend on
accurate sensor data.